December 28, 2023

12 Ways Of Using Generative AI in E-Commerce

Learn about the best cases of e-commerce conversational AI and e-commerce chatbot examples and how they can benefit your business.

Written by
Serhii Uspenskyi

Table of Contents

Generative AI has been around only for a decade. Despite its short existence, the technology significantly impacted the e-commerce industry. Companies have been blown away by its ability to create content out of thin air, which is clearly reflected in the latest statistics.

Today, 80% of e-commerce companies use or plan to implement AI chatbots. By 2030, the AI market is expected to reach $110.8 billion. Additionally, generative AI will be responsible for 10% of all data generated in 2025. This article concerns the generative AI-e-commerce combination and its role in business improvement.

What Is Generative AI?

This term refers to smart software solutions capable of making different types of content. It includes text, images, video, and audio materials. The world’s most popular e-commerce chatbot examples include ChatGPT, Google Bard, Bing, and Dall-E. These solutions use deep learning methods. These include autoencoders, generative adversarial networks, and neural networks. 

The main components of generative AI tools are large language models. They allow solutions to comprehend and process different types of language input types. LLMs are responsible for understanding the context, nuances, and semantics of human speech. This knowledge lets AI products produce human-like output.

They help e-commerce conversational AI products learn data patterns and generate new content based on them. They analyze input information, compare it with the training data, and predict elements that should appear in the output. These solutions have various applications, making them popular in different industries, including e-commerce.

AI & E-Commerce: Benefits

Like all products, generative AI e-commerce solutions have a mix of benefits and drawbacks. Businesses should be aware of them to use generative AI products more efficiently. First, let’s take a look at the positive aspects of this technology:

  1. Fast Data Analysis

Companies use AI in commerce to analyze large volumes of customer data. These solutions analyze client buying behavior, preferences, and patterns. This information is invaluable for marketing plans, improving customer relations, and product recommendations. This results in better customer loyalty and higher conversion rates.

  1. Enhanced Client Targeting

People are more inclined to shop when offered a personalized shopping experience. Modern e-commerce conversational AI products automatically gather customer insights. This makes the shopping experience more personal for all clients. An engaged client is a satisfied client who’s more likely to buy from the company again.

  1. Improved Customer Service

AI lies at the heart of modern e-commerce chatbot examples and virtual assistants. These solutions comprehend, analyze, and respond to user requests. In 2023, AI reduced 33% of customer service costs. This is because generative helpers are always online and never tire.

  1. Process Optimization

Generative AI solutions make company operations more affordable and efficient through process automation. Modern AI commerce products handle content creation, marketing campaign optimization, and data analysis. This allows employees to focus on creative and decision-making tasks that need a personal touch and human insight.

  1. Streamlined Logistics

Businesses use generative AI solutions for demand forecasting and item replenishment. These solutions analyze sales and seasonal patterns to keep the inventory stocked. In some cases, AI helped companies decrease lost sales by 35%. Generative AI helps businesses get new products, resulting in 15% reduced costs.

  1. Intelligent Fraud Detection

Solutions related to e-commerce conversational AI solutions help companies detect fraudulent activity. They learn about scamming techniques used by malicious actors and pinpoint suspicious transactions. This allows security teams to address fraud and notify people who became its target quickly.

AI & E-Commerce: Drawbacks

Now that we’ve established the benefits of generative AI e-commerce products, we should also address their drawbacks. Companies must find an effective way to deal with them to unlock the full potential of generative AI solutions.

  1. AI Bias

Since humans train artificial intelligence solutions, they can take on their biases. In some cases, these preconceptions get amplified. Company data that shows preferences for specific demographics will train AI commerce solutions to do the same. To make AI products more fair, businesses must regularly comb algorithms for signs of bias. They should also work with AI development companies that follow ethical practices. 

  1. Poor Data Quality

The effectiveness of AI models relies on the quality of information that’s used in training them. They can produce flawed or biased results if the information is inaccurate or incomplete. When implementing AI in commerce, businesses must have all data in the same format. This information is used in fine-tuning large language models.

  1. Ethical and Regulatory Challenges

Finally, businesses must navigate the world of regulations concerning AI use and data privacy. The EU, the US, and China are working on rules and regulations for these technologies. E-commerce companies must embrace data governance measures and adopt responsible practices.

12 Use Cases For Generative AI in E-Commerce

All businesses have their unique applications for AI products. Currently, the most popular use cases for this technology include:

  1. Automated Content Creation

Companies use AI to make product descriptions, social media posts, reviews, and other written content. This cuts down time and labor efforts and results in unique materials.

  1. Customer Support Chatbots.

Modern e-commerce chatbot examples provide personal and life-like answers to customer requests. They make customer service available 24/7. This leads to quicker issue resolution and better client satisfaction.

  1. Recommendation Systems

Generative AI solutions suggest products based on client preferences, browsing history, and buying behavior. These recommendations lead to better upselling and cross-selling opportunities.

  1. Personalized Marketing Campaigns

Companies use generative AI to create banners, ads, and visuals tailored to particular customers. Such targeted campaigns lead to better engagement and conversion rates.

  1. Product Image Generation

Advanced generative AI programs create detailed product images. This can be used when working on prototypes and launching new items. Companies get their hands on quality visual content without ordering expensive photoshoots.

  1. Virtual Try-Ons

Modern AI commerce products let clients try on products from the comfort of their homes. They include accessories, footwear, and apparel. This gives them a better idea of how these items will look on their person.

  1. Better SEO Strategies

Advanced AI generative tools tailor unique materials using the most relevant keywords in the text and metadata. This improves the chances of product pages appearing at the top of search results.

  1. Individual In-Person Checkout Experience

E-commerce companies use generative AI to make order summaries and deliver them to store managers at the checkout.

  1. Improved Inventory Management

Businesses use modern AI solutions to track orders and provide information about them. These products are also excellent when it comes to efficient inventory management.

  1. Price Optimization

Generative AI tools track and compare competitor prices and analyze supply and demand trends. This leads to optimized price tags on different products.

  1. Product Design

Companies use this technology to make new products based on current items. This streamlines development and makes cutting-edge creation more effective.

  1. Visual Search

More businesses use AI in commerce to allow users to look for items via visual search. Instead of using text, generative AI utilizes images to find relevant items in the business’s inventory.

How to Implement Artificial Intelligence Into E-commerce

Companies can get carried away and order generative AI e-commerce applications they don’t need. Companies should make a road map for AI implementation to ensure that they stay on track and save time and money on this endeavor. There are several components to this process.

  1. Make a strategy.

Before any work starts, consider what your business wants to achieve with AI. The integration should have practical benefits for the company. It’s better to discuss this plan with all department heads and establish areas where this technology will be most efficient.

  1. Find the best use cases.

Businesses should focus on AI applications aligned with their goals, data capabilities, and existing AI models. Concentrate on use cases that increase revenue generation and have enough structured information to build AI solutions.

  1. Get outside help.

Most companies lack the technical expertise to develop AI products successfully. Find an outsource developer specializing in this field and tell them about your project idea. They will help bring an MVP version of your product to life. 

  1. Work on the solution.

Once there’s a working prototype in place, work with the programmers on the full-scale version. It may take some time to fine-tune and get deployed. But, this time will be worth the wait once generative AI starts working its magic.

Final Thoughts

This technology is still new to the e-commerce field. Despite this fact, it shows great results in improving conversion rates, automating company processes, and adding a personal touch to customer experience. If you wish to enhance the shopping process on your e-commerce website or apps, let us know.

Customer retention is the key

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What are the most relevant factors to consider?

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Don’t overspend on growth marketing without good retention rates

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What’s the ideal customer retention rate?

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Next steps to increase your customer retention

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